Literature DB >> 11996348

Longitudinal dispersion coefficients in natural channels.

Seyed M Kashefipour1, Roger A Falconer.   

Abstract

Details are given herein, of the development of an equation for predicting the longitudinal dispersion coefficient in riverine flows, based on 81 sets of measured data, and obtained from 30 rivers in the USA. This equation relates the dispersion coefficient to the hydraulic and geometric parameters of the flow and has been derived using dimensional and regression analysis, with a high correlation coefficient (i.e. R2 = 0.84). The formulation has been compared with many other existing empirical equations, frequently used to predict the longitudinal dispersion coefficient in riverine flows, with the comparisons based on four different statistical methods. These statistical comparisons have shown that the new equation appears to be more accurate than the other equations considered. The new dispersion equation was then linearly combined with a similar equation recently proposed by Seo and Cheong (J. Hydraul. Eng. ASCE 124 (1998) 25) and this combined equation was then also analysed using statistical methods. The existing empirical equations used to estimate the longitudinal dispersion coefficient and the new equations proposed in this study were included in the advective dispersion equation to predict the suspended sediment concentrations at three sites in the Humber Estuary sited along the northeast coast of England. The average percentage errors between the predicted- and measured-field data for the proposed new dispersion equations were less than those obtained using the previously documented equations.

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Year:  2002        PMID: 11996348     DOI: 10.1016/s0043-1354(01)00351-7

Source DB:  PubMed          Journal:  Water Res        ISSN: 0043-1354            Impact factor:   11.236


  2 in total

1.  Estimating the microbiological risks associated with inland flood events: Bridging theory and models of pathogen transport.

Authors:  Philip A Collender; Olivia C Cooke; Lee D Bryant; Thomas R Kjeldsen; Justin V Remais
Journal:  Crit Rev Environ Sci Technol       Date:  2016-12-09       Impact factor: 12.561

2.  Improving one-dimensional pollution dispersion modeling in rivers using ANFIS and ANN-based GA optimized models.

Authors:  Akram Seifi; Hossien Riahi-Madvar
Journal:  Environ Sci Pollut Res Int       Date:  2018-11-11       Impact factor: 4.223

  2 in total

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